To verify the effectiveness of our MIW-weighted model (re)training scheme, we compare our AMPL with its variant of training the model only at the beginning using MLE, i.e., No Weights (dubbed as NW).
As shown in Figure REF , the performance of the NW variant is generally worse than the main method (dubbed as Main) on all three domains.
The performance difference is especially significant on the Maze2D domain.
In this domain, the policy is required to “stitch together collected subtrajectories to find the shortest path to the evaluation goal” [1]}. As a result, the state-action distribution induced by a good policy should be different from the distribution of the offline-data.
Table REF  in Appendix  confirms the benefit of the MIW-weighted model (re)training.
Indeed, on the majority of datasets, the NW variant not only shows worse performance, but also has a larger standard deviation relative to the mean score.
